Deducting abnormalities in chest X-rays using Gabor filters and deep neural network (DNN)
Chest X-rays are widely used as a diagnostic tool to detect respiratory diseases. The complexity of the texture and structures shown in the resulting images can make their interpretation difficult. A more accurate interpretation would help diagnose respiratory diseases earlier, resulting in more eff...
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2024
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| Online Access: | http://journalarticle.ukm.my/25736/ http://journalarticle.ukm.my/25736/1/16.pdf |
| _version_ | 1848816436868284416 |
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| author | Mohammed Sayim Khalil, |
| author_facet | Mohammed Sayim Khalil, |
| author_sort | Mohammed Sayim Khalil, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Chest X-rays are widely used as a diagnostic tool to detect respiratory diseases. The complexity of the texture and structures shown in the resulting images can make their interpretation difficult. A more accurate interpretation would help diagnose respiratory diseases earlier, resulting in more effective and timely treatment. In this research, the author proposes a new method for detecting abnormalities in chest X-ray images using Gabor filters and artificial intelligence (AI). Gabor filters are a type of filter that can be used to extract texture features from images. These features can then be used to train a deep neural network (DNN) to detect abnormalities in chest X-rays. The method demonstrates the effectiveness of its approach on a dataset of chest X-rays from the National Institutes of Health (NIH) Chest X-ray Dataset. The method achieved an accuracy of 79% in detecting abnormalities, suggesting that this novel method has the potential to help detect respiratory diseases early and, ultimately, improve the lives of the millions afflicted by such diseases. |
| first_indexed | 2025-11-15T01:05:51Z |
| format | Article |
| id | oai:generic.eprints.org:25736 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:05:51Z |
| publishDate | 2024 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:257362025-08-12T01:51:40Z http://journalarticle.ukm.my/25736/ Deducting abnormalities in chest X-rays using Gabor filters and deep neural network (DNN) Mohammed Sayim Khalil, Chest X-rays are widely used as a diagnostic tool to detect respiratory diseases. The complexity of the texture and structures shown in the resulting images can make their interpretation difficult. A more accurate interpretation would help diagnose respiratory diseases earlier, resulting in more effective and timely treatment. In this research, the author proposes a new method for detecting abnormalities in chest X-ray images using Gabor filters and artificial intelligence (AI). Gabor filters are a type of filter that can be used to extract texture features from images. These features can then be used to train a deep neural network (DNN) to detect abnormalities in chest X-rays. The method demonstrates the effectiveness of its approach on a dataset of chest X-rays from the National Institutes of Health (NIH) Chest X-ray Dataset. The method achieved an accuracy of 79% in detecting abnormalities, suggesting that this novel method has the potential to help detect respiratory diseases early and, ultimately, improve the lives of the millions afflicted by such diseases. Penerbit Universiti Kebangsaan Malaysia 2024-09 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/25736/1/16.pdf Mohammed Sayim Khalil, (2024) Deducting abnormalities in chest X-rays using Gabor filters and deep neural network (DNN). Jurnal Kejuruteraan, 36 (5). pp. 1965-1972. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-3605-2024/ |
| spellingShingle | Mohammed Sayim Khalil, Deducting abnormalities in chest X-rays using Gabor filters and deep neural network (DNN) |
| title | Deducting abnormalities in chest X-rays using Gabor filters and deep neural network (DNN) |
| title_full | Deducting abnormalities in chest X-rays using Gabor filters and deep neural network (DNN) |
| title_fullStr | Deducting abnormalities in chest X-rays using Gabor filters and deep neural network (DNN) |
| title_full_unstemmed | Deducting abnormalities in chest X-rays using Gabor filters and deep neural network (DNN) |
| title_short | Deducting abnormalities in chest X-rays using Gabor filters and deep neural network (DNN) |
| title_sort | deducting abnormalities in chest x-rays using gabor filters and deep neural network (dnn) |
| url | http://journalarticle.ukm.my/25736/ http://journalarticle.ukm.my/25736/ http://journalarticle.ukm.my/25736/1/16.pdf |